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Abstract

Background

Early microbial colonization of the gut reduces the incidence of infectious, inflammatory
and autoimmune diseases. Recent population studies reveal that childhood hygiene is
a significant risk factor for development of inflammatory bowel disease, thereby reinforcing
the hygiene hypothesis and the potential importance of microbial colonization during
early life. The extent to which early-life environment impacts on microbial diversity
of the adult gut and subsequent immune processes has not been comprehensively investigated
thus far. We addressed this important question using the pig as a model to evaluate
the impact of early-life environment on microbe/host gut interactions during development.

Results

Genetically-related piglets were housed in either indoor or outdoor environments or
in experimental isolators. Analysis of over 3,000 16S rRNA sequences revealed major
differences in mucosa-adherent microbial diversity in the ileum of adult pigs attributable
to differences in early-life environment. Pigs housed in a natural outdoor environment
showed a dominance of Firmicutes, in particular Lactobacillus, whereas animals housed in a hygienic indoor environment had reduced Lactobacillus and higher numbers of potentially pathogenic phylotypes. Our analysis revealed a strong
negative correlation between the abundance of Firmicutes and pathogenic bacterial
populations in the gut. These differences were exaggerated in animals housed in experimental
isolators. Affymetrix microarray technology and Real-time Polymerase Chain Reaction
revealed significant gut-specific gene responses also related to early-life environment.
Significantly, indoor-housed pigs displayed increased expression of Type 1 interferon
genes, Major Histocompatibility Complex class I and several chemokines. Gene Ontology
and pathway analysis further confirmed these results.

Conclusion

Early-life environment significantly affects both microbial composition of the adult
gut and mucosal innate immune function. We observed that a microbiota dominated by
lactobacilli may function to maintain mucosal immune homeostasis and limit pathogen
colonization.

Background

The gastrointestinal tract contains an immense number of micro-organisms, collectively
known as the microbiota. The major functions of the microbiota include degrading dietary
compounds, influencing nutrient partitioning and lipid metabolism, providing essential
nutrients generated as a result of microbial metabolism, protecting against invading
pathogens and stimulating gut morphology [1-4]. The gut microbiota also plays an important role in maintaining immune function.
Recent work suggests that the commensal microbiota influences processes as complex
as pathogen colonization, immune development and homeostasis, T cell differentiation,
inflammation, repair and angiogenesis [5-8].

The impact of the microbiota on host immunity is thought to be critically regulated
in early life and inappropriate exposure to bacteria during this developmental window has been linked to the increased incidence
of infectious, inflammatory and autoimmune diseases [9-11]. Clearly, the neonatal period is a critical time for gut colonization, and can be
affected by numerous factors including gestational age, birth environment, mode of
delivery, nutrition and antibiotic use [12,13].

The increase in immune-mediated disorders, particularly in Westernized countries,
has led to the so-called Hygiene Hypothesis, which postulates that the growing incidence of immune-mediated diseases is the consequence
of reduced infection and exposure to microbes during early childhood [14]. In this context, the high-hygiene status of western lifestyle, decreased infection
rates and reduced bacterial load as a result of widespread use of vaccines and antibiotics
are likely to be important contributory factors [15]. Animal models have provided some insight into immune-disease aetiology: animals
susceptible to autoimmune disease have an increased incidence and severity of disease
when bred under germ-free conditions whereas disease is prevented when the animals
are exposed to bacteria [16]. This evidence supports the notion that, in addition to naturally-acquired infections,
colonization by the normal commensal microbiota is an important factor limiting the
incidences of immune-mediated diseases. Consistent with this is the growing awareness
of the importance of the commensal microbiota in immune education in early life [8], which appears to involve complex mechanisms of host-bacterial crosstalk [5,17-21].

In the current study we have investigated potential interactions between the rearing
environment, gut microbiota and immune function in the developing pig gut using molecular
methods to evaluate both microbial diversity and host immune gene expression. Microbial
diversity in the gastrointestinal tract of these animals was characterized by sequence
analysis of 16S rRNA gene libraries. Specific responses in transcriptome expression
patterns of gut ileal tissue were studied using Affymetrix GeneChip Porcine Genome
microarrays (Affymetrix, Santa Clara, CA). Biomarkers associated with immune function
and altered by rearing environment were identified and investigated more thoroughly
by Real-time Polymerase Chain Reaction (PCR).

Results

Mucosal microbial diversity in the ileum of pigs from different environments

We investigated the influence of environmentally-acquired bacteria on the composition
of the adult mucosa-adherent ileal microbiota in the pig. Animals were housed in an
indoor (IN) or an outdoor facility (OUT), as well as in individual isolator units
receiving daily doses of antibiotics (IR). Mucosa-adherent bacterial samples from
the ileum and fecal samples were collected from all experimental animals at day 56.
In addition to this, fecal samples were taken from adult sows from both the indoor
(INS) and outdoor (OUTS) environments to confirm 'environment' as the major factor
contributing to the experimental differences. Microbial composition of the ileum was
examined by calculation of diversity indices and analysis of the phylogenetic distribution
of 16S rRNA gene sequences derived from clone libraries of each treatment. After quality
control, a total of 3,089 validated clones were analyzed.

Diversity Measures

We first investigated the effects of environment and high-hygiene status on a number
of bacterial diversity indices. Estimates of diversity, richness and library coverage
for the 16S rRNA clone libraries from IN, OUT and IR are shown in Table 1. Species richness, estimated by Chao1, was highest in the IR and IN groups but lower
in the OUT group. Good's coverage was 90.97 to 93.47% for all three treatment groups,
with the lowest coverage in IR libraries. Rarefaction analysis of clone libraries
confirmed these findings and suggested that the IR and IN groups possessed the most
diverse mucosa-adherent bacterial community, whereas the OUT group showed lower microbial
diversity (Figure 1).

Figure 1.Treatment-based rarefaction curves of 16S rRNA gene libraries. The rarefaction curves from IN (pink), OUT (green) and IR (black) animals were generated
by plotting the number of phylotypes (OTUs; defined at 99% sequence identity) against
the number of clones sequenced. The shape of the curves of observed phylotype richness
indicates a trend of diminishing chance of finding new phylotypes as sampling continues.

Collector's curves of the observed and estimated phylotype richness are shown in Figure
2A-C. Each curve reflects the series of observed or estimated richness values obtained
as more clones were added to the data set. After an initial steep rise, the curves
level out, suggesting that the majority of phylotypes in the treatment groups were
adequately sampled. In the early stages of sampling and clone sequencing, both Chao1
and abundance-based coverage estimator (ACE) showed a sharp increase, together with
the observed phylotype number, in the IN group (Figure 2A). After the sampling of about 190 clones, the gap between the observed and estimated
phylotype richness was relatively constant, indicating repeated sampling of same phylotypes
within samples. In the OUT group, the gap between the observed and estimated phylotype
richness was constant after the sampling point of 110 clones (Figure 2B). The difference between the estimated and observed phylotype richness was highest
in the IR mucosal libraries. Novel phylotypes continued to be identified up to the
end of sampling (Figure 2C).

Figure 2.Collector's curves of the observed and estimated phylotype richness of 16S rRNA gene
libraries. Collector's curves of the observed (blue) and estimated (ACE (pink) and Chao1 (yellow))
phylotype richness calculated for IN (A), OUT (B) and IR (C) at 99% level. Each curve reflects observed or estimated richness values obtained as
more clones are added to the data set. After an initial steep rise, the curves level
out suggesting that a majority of clones in the treatment groups have been sampled.
Differences between the estimates and observed phylotype richness were highest in
the IR group. Novel phylotypes continued to be identified up to the end of sampling
in this group.

Phylogenetic affiliation of 16S rRNA gene sequences

Phylogenetic analysis was performed to establish taxonomic positioning of obtained
sequences. All 16S rRNA gene sequences from the mucosa-adherent ileal and fecal samples
were subjected to the Ribosomal Database Project (RDP) Classifier analysis (95% confidence
threshold). Based on the classification results, the majority of clones were assigned
to four phyla: Firmicutes (69.7% of all sequences), Proteobacteria (17.7%), Bacteroidetes
(11.4%), and Actinobacteria (0.5%) (Figure 3 and Table 2). The two major phyla, Firmicutes and Bacteroidetes, were significantly different
between the libraries: the Firmicutes showed a significant increase in the OUT group
compared to the IR group, while the Bacteroidetes were significantly increased in
both the INS and OUTS fecal libraries compared to the mucosa-adherent ileal libraries.

Figure 3.Phylogenetic distribution of clones obtained from mucosa-adherent ileal and fecal
samples in different housing environments. The majority of clones were assigned to the phyla Firmicutes, Proteobacteria, Bacteroidetes,
and Actinobacteria. The Firmicutes phylum was significantly increased in the OUT group
compared to the IR group (P < 0.05). The Bacteroides phylum was significantly increased in both the INS and OUTS
fecal libraries compared to the mucosa-adherent ileal libraries. Values are expressed
as means ± SEM (N = 4).

Firmicutes

Seventy percent of all sequences were affiliated with the Firmicutes phylum. The outdoor
environment favoured the expansion of Firmicutes compared to the hygienic environment
(Figure 3). At the lower taxa level this difference was even more pronounced.

A large number of sequenced clones fell into the Bacilli class. The most abundant
order was Lactobacillales, which was dominated by Lactobacillaceae, but also contained
Streptococcaceae, Leuconostocaceae, Enterococcaceae, Carnobacteriaceae and Aerococcaceae,
although present in lower abundance.

The Lactobacillaceae family in the OUT group (77.2% of sequences) consisted of a small
number of operational taxonomic units (OTUs), including Lactobacillus reuteri, L. amylovorous LAB31, L. johnsonii, L. delbrueckii subsp. bulgaricus, L. salivarius and L. mucosae (Figure 4). In contrast, the IN library contained only 12.8% Lactobacillaceae-affiliated clones,
although phylotypes were similar to those observed in the OUT group. L. reuteri, L. delbrueckii and L. johnsonii were all significantly decreased compared to the OUT group. The high-hygiene conditions
associated with IR exacerbated these differences. L. amylovorous LAB31 and L. brevis were present in very low abundance in the IR libraries (3.58% of sequences) whereas
L. reuteri, L. delbrueckii subsp. bulgaricus, L. johnsonii and L. mucosae were not detected in this treatment group.

Figure 4.Abundance of lactobacilli in mucosa-adherent ileal and fecal samples in different
housing environments. The Lactobacillaceae family included L. reuteri, L. amylovorous LAB31, L. johnsonii, L. delbrueckii subsp. bulgaricus, L salivarius and L. mucosae. L. reuteri, L. delbrueckii and L. johnsonii were all significantly lower in the IN and IR groups compared to the OUT group. Values
are expressed as means ± SEM (N = 4).

The observed differences in Lactobacillus levels between the IR and OUT group were confirmed by enumeration of bacteria in gut
contents of both ileum and colon on de Man, Rogosa and Sharpe (MRS) agar. The OUT
group had three to four log10 colony forming units lactobacilli/g more than the IR group, thus validating the 16S
rRNA gene library results (Additional file 1).

Members of the Clostridia class were present in all treatment groups with 28.2% of
all sequenced clones classified as Clostridia. Interestingly, pigs raised in the indoor
environment showed the highest abundance of this class.

Clostridiaceae-affiliated clones were highly abundant in the IN group and mainly identified
as uncultured species. Clostridium beijerinckii NCIMB 8052 was significantly elevated in the IN libraries compared to the OUT and
IR libraries (Figure 5). The indoor environment also favoured the expansion of the bacterial clone HH_aai33h06
(EU775688) on the ileal mucosa compared to the outdoor environment.

Figure 5.Significantly affected bacterial clones in the mucosa-adherent ileum of animals in
different housing environments. Clostridium beijerinckii NCIMB 8052, as well as uncultured bacterial clone BARB_aaa01f06, BARB_aaa02d03 and
HH_aai33h06 were significantly decreased in the OUT library compared to the IN library.
Uncultured bacterial clone BARB_aaa01f06 and Clostridium beijerinckii NCIMB 8052 were also significantly decreased in the IR group. Values are expressed
as means ± SEM (N = 4).

The Peptostreptoccocaceae family was another abundant member of the Clostridia class,
accounting for 19.1% of the sequenced clones in all treatment groups. The indoor environment
favoured the expansion of Peptostreptoccocaceae. Seven predominant OTUs, represented
by mainly uncultured clones, were identified. Sequences of uncultured clones BARB_aaa02d03
were significantly higher in the IN and IR groups compared to the OUT group and were
not detected in the indoor fecal libraries (Figure 5). This possibly points to a preferential colonization of the ileal mucosa in the
indoor environment. Uncultured bacterium clone BARB_aaa01f06 was significantly increased
in the ileal mucosal libraries of the IN group compared to the OUT and IR groups,
indicating a potential antibiotic sensitivity of this bacterium.

Bacteroidetes

Bacteroidetes were found in all libraries but in different abundance. The most abundant
group within this phylum was represented by members of the Prevotellaceae family,
followed by Porphyromonadaceae, Bacteroidaceae and, to a lesser extent, Sphingobacteriaceae
and Flavobacteriaceae.

All 16S rRNA gene libraries contained members of Prevotellaceae, yet they were most
prevalent in the indoor environment, particularly in fecal libraries. High-hygiene
conditions increased the numbers of Prevotellaceae on the ileal mucosa.

Porphyromonadaceae were mainly obtained from the fecal libraries of both farms. Most
clones had only 97% similarity to previously isolated clones, specifically the Porphyromonadaceae
bacterium sp DJF_B175 (EU728718) and uncultured bacterial clones (EU472597, EU472617
and EU461958).

Bacteroidaceae were exclusively obtained from the indoor environment. Within the IN
and IR groups, these included Bacteroides vulgatus (CP000139) and uncultured bacterial clones (EF403095, EF403812, EU779318 and DQ800210).
In the IN fecal libraries two species were related to B. propionifaciens (AB264625.2) and uncultured bacterium clone p-240-o5 (AF371909).

Proteobacteria

Eighteen percent of all clones were placed into the Proteobacteria phylum. γ-proteobacteria
and ε-proteobacteria were the most abundant groups, while members of the α- and β-proteobacteria
were found only sporadically.

Twenty-eight γ-proteobacteria clones were obtained from the OUT mucosa-adherent libraries.
These included E. coli, Actinobacillus minor and A. porcinus. Six OTUs belonging to Actinobacillus spp. were predominately present in the IN group, including Actinobacillus minor, A. porcinus strains H1498/H1215 and A. rossii strain JF1390. This clone has been isolated from the intestine and reproductive tract
of pigs and is considered an opportunistic pathogen implicated in spontaneous abortion.

High-hygiene status increased the number of γ-proteobacteria on the ileal mucosa.
All 16S rRNA gene libraries from the IR group contained members of the γ-proteobacteria
class and grouped mainly with Enterobacteriaceae, including sequences identified as
E. coli spp. with pathogenic properties which may pose a health risk for the young pig as
well as the human population.

Members of the ε-proteobacteria were the second most abundant group within the Proteobacteria
phylum and were represented by two major bacterial families, Helicobacteraceae and
Campylobacteraceae. Most clones were obtained from the IN group and included bacteria
of recognized pathogenic phenotype (13% of IN sequences).

Transcriptomic analysis of gene expression patterns in the ileum of pigs from different
environments

While the comprehensive profiling of the mucosa-adherent microbial community revealed
large differences in composition attributable to differences in housing environment,
a key goal of this study was to determine whether this translated into different host-specific
gene responses. Therefore, an Affymetrix GeneChip microarray analysis was conducted
on ileum tissue from the same site used for 16S rRNA gene library construction.

Effects of treatment extremes on gene expression

Perhaps not surprisingly, mucosa-adherent microbial diversity in the ileum was most
affected by experimental isolator housing, as this constituted a high-hygiene environment.
To ascertain the differences in host-specific transcriptional responses between this
treatment and the natural outdoor environment (treatment extremes), Affymetrix microarray analysis was performed on the comparison IR versus OUT at
day 5 (neonatal stage), day 28 (weaning age) and day 56 (nearing maturity).

Seventy-four probesets were differentially expressed (P < 0.01 and -2 ≤ fold change ≥ 2) at the neonatal stage (Figure 6 and Additional file 2). Fifty-six of these genes were highly expressed in the IR group, while 18 genes
were higher in the OUT group. Interestingly, within the IR gene set, increased expression
of genes that are closely linked to Type 1 interferon (IFN) signalling was observed.
These genes included IRF7, FAM14A, UBE2L6, GBP2 and USP18. Some of the most highly-regulated genes (up to 11-fold higher in the IR group) were
viperin, a tightly regulated ISGF3 target gene [22], and IRP6, a pig-specific gene homologous to human viperin. Another group showing increased
expression in the IR group included 15 genes involved in cholesterol synthesis, such
as DHCR7, DHC24, SC5DL, HMGCS1, CYP51A1 and ERG1. Genes of interest showing higher expression in the OUT group compared to the IR
group included TLR2 as well as HBB and HBA1, both of which code for haemoglobin proteins.

Figure 6.Differentially expressed genes in the ileum of animals housed in different environments. Differentially expressed genes at each time-point are shown for the two treatment
comparisons (P < 0.01, -2 ≤ fold change ≥ 2, N = 6). Microbiota differences between the treatment
groups were associated with large differences in gene expression in the ileum.

Additional file 2.Transcripts differentially expressed between treatments at all three time-points. Differentially expressed genes at each time point are shown for the comparison IR
versus OUT, and IN versus OUT (P < 0.01, -2 ≤ fold change ≥ 2, N = 6).

At day 28, 111 genes were differentially expressed (Figure 6 and Additional file 2). Twenty-one of the 83 transcripts expressed at higher levels in IR were the same
as those found at day 5, and included the IFN-induced genes IRF7 and GBP2. Several other Type 1 IFN-induced genes (G1P2, IFIT2, IFIT3, MX2, ISG20 and IFITM3) were higher in IR animals compared to OUT animals, indicating a consistent treatment
effect on Type 1 IFN signalling pathways. Also in common with the day 5 gene expression
set, nine cholesterol synthesis genes were increased in the IR group. Consistent with
these findings, microbiota-driven effects on cholesterol metabolism and trafficking
have been previously documented [23]. Other transcripts expressed higher in the IR group compared to the OUT group included
the chemokines CXCL12, CCL28, CCL2, CCL8 and CXCL9, the chemokine receptor CCR5 and the chemokine ligand CCL4L. PMP22 was increased in the OUT group. This gene is co-expressed with occludin and zona occludens
1 at tight junctions in epithelial cells [24].

Sixty-six genes were differentially expressed between IR and OUT at day 56 (Figure
6 and Additional file 2). Some of the genes showing higher expression in the IR group included KAI1, CEBPB, LTB4DH, COL14A1 and COL1A2. Changes in CEBPB (CCAAT/enhancer-binding protein beta) expression in IR animals may be functionally
important as this gene is involved in the regulation of inflammatory responses [25]. Notably, a group of T-cell-related genes was increased in OUT animals, including
TCA_HUMAN, LY96, CD8A, TRGV9, LCP1, LCP2, CXCL9 and TEC, all of which are involved in T cell signalling, expansion, activation and trafficking.
Other highly expressed transcripts in the OUT group included EGR1, SELL (important for leukocyte-endothelial cell interactions), PIGR (poly-Ig receptor) and PIK3CG.

Consistently, PDK4 was higher in the OUT group compared to the IR group at all three time-points. PDK4 has an important function in glucose metabolism, and its expression is regulated by
glucocorticoids, retinoic acid and insulin; however, its potential relevance in host-microbe
interactions is currently unknown.

Biological pathway analysis revealed that a large number of Immune response pathways were affected (Table 3 and Figure 7A). Other highly represented pathways included G-protein and Congenital, hereditary and neonatal diseases and abnormalities. Consistent with the analysis of individual gene data, the pathway for Immune response-IFN alpha/beta signalling was increased at day 28 and day 56 in the IR group compared to the OUT group. Immune response-Antigen presentation by MHC class l was affected at all three time-points and also higher in IR compared to OUT. Gene
Ontology (GO)-enrichment analysis (Table 4) further confirmed these findings. While a number of GO categories were consistently
affected by treatment, including Immune response (GO:0002376), the major biological process affected was Antigen processing and presentation (GO:0019882). Other affected GO-processes were Antigen processing and presentation of peptide antigen via MHC class I (GO:0002474), Antigen processing and presentation of peptide antigen (GO:0048002) and Antigen processing and presentation of peptide or polysaccharide antigen via MHC class
II (GO:0002504).

Figure 7.MetaCore pathway analysis of differentially expressed genes of animals housed in different
environments. Differentially expressed genes (P < 0.05) were imported into GeneGo MetaCore analytical software to determine significantly
enriched canonical pathways in each group. Data represent the distribution in cell
process categories of statistically significantly enriched pathways (P < 0.05) of the comparisons IR vs OUT (A) and IN vs OUT (B). Most pathways from both comparisons group into five categories: G-proteins; G-protein
coupled receptor; congenital, hereditary and neonatal diseases and abnormalities;
immune response; and development. Note that there is redundancy in category allocation.

Effects of housing environment on gene expression

Differences in ileal mucosa-adherent microbial composition between the IR group and
the OUT group were associated with large host-specific transcriptional differences
in the ileum. We next set out to assess whether the microbial differences associated
with the IN and OUT environments had a similar impact on the gut transcriptome of
the pig. While the number of differentially expressed genes between IN and OUT housed
animals was smaller than between the treatment extremes (i.e. IR and OUT), similar
trends could be discerned.

In the neonatal pig, the expression levels of 13 probesets were differentially expressed
between the IN and OUT animals (Figure 6 and Additional file 2). Nine genes were higher in IN animals, and this included CXCL9, which is involved in T cell trafficking. Four genes showed higher expression in
OUT animals, including TFRC.

In weaning animals, 42 genes were differentially expressed between the two rearing
environments (Figure 6 and Additional file 2). Twelve transcripts were higher in IN animals, including TAFA2 (distantly related to CCL3), CCR1 and CXCR4. Of the 30 genes that were higher in the OUT group, genes of interest included PMP22, CNKSR1, TJP4 and LTBR (all increased between two- and three-fold).

The largest differences in gene expression were observed at day 56, when 71 genes
were differentially expressed between the treatments (Figure 6 and Additional file 2). Transcripts increased in IN animals (60 probesets in total) included three Type
1 IFN-inducible genes (IFRD1, OAS1 and IFIT2). The antibacterial peptide genes LYZ (Lysozyme C precursor), PI3 (elafin precursor) and BPI (bactericidal permeability-increasing protein precursor) were increased 6.92-, 6.8-
and 2.93-fold, respectively, in IN animals compared to OUT animals and may contribute
to the observed differences in microbiota composition between these groups. Furthermore,
these peptides appear to maintain gut homeostasis as evidenced by their aberrant expression
in Crohn's Disease [26] and Ulcerative Colitis [27]. CCL8 (a monocyte chemotactic protein) was also higher in the IN group. Some of the 11
genes increased in OUT animals were PMP22 and SELL, in accordance with the observations from the IR and OUT comparison.

The most affected pathways belonged to Immune response, G-protein and Congenital, hereditary, and neonatal diseases and abnormalities (Table 5 and Figure 7B), as observed previously in the treatment extremes comparison.

Real-time quantitative PCR to analyze differentially expressed genes

Real-time PCR was performed for CCL28, CCL8, CXCL12, CXCL9, CXCR4, IFIT2, FKBP5, IRF7, IRP6, MT1J, MX, PDK4, PI3, SELL, SQLE and TFRC. These genes were selected from the gene expression data set both because they showed
significant changes and because of their involvement in key immune system pathways.
Verification of the true differential expression between treatment groups of these
genes by Real-time PCR was therefore considered essential for further biological interpretation.

The subsequent correlation between Affymetrix microarray and Real-time PCR data (R2 = 0.8405; P < 0.001; Figure 8) was positive, further substantiating the biological importance of the selected genes
and identified pathways. Real-time PCR verification for the comparison IR versus OUT
showed that direction and magnitude of fold change correlated well with the Affymetrix
microarray results (Table 6). In some cases the fold changes detected by Real-time PCR were lower than those
observed by microarray analysis.

Figure 8.Scatterplot of concurrence between Affymetrix microarray data and Real-time PCR data. Correlation between mean fold change values of both comparisons obtained by Affymetrix
microarray analysis and Real-time PCR analysis. The diagonal line represents the power
trendline (R2 = 0.8405).

The differentially expressed genes from the IN versus OUT comparison were examined
by Real-time PCR and again correlated well with the Affymetrix microarray results
(Table 6). Only CXCR4 expression in the IN group showed disagreement between the two platforms, as it was
increased using microarray analysis, and decreased using Real-time PCR.

Discussion

The current study sought to investigate the effects of environmental hygiene on microbial
colonization and composition of the gut microbiota. Additionally, transcriptomic profiling
was performed to assess the impact of environmental hygiene on gene expression, in
particular those genes and pathways associated with immune function. Both indoor and
isolator (representing urban lifestyle and high-hygiene status, respectively), and
outdoor (representing rural lifestyle and low-hygiene status) conditions were compared
using pigs as an experimental model.

Using extensive analysis of 16S rRNA gene libraries our study categorically revealed
that early-life environment has a major impact on microbial diversity and that these
differences are sustainable throughout adult life. Many of the bacterial phylotypes
identified in our study are commonly found in the human and animal gastrointestinal
tract [28-30]. Our results also identified that only 3.3% of the clones had less than 97% sequence
similarity to existing database entries.

A major finding of the current study was the significant increase in the Firmicutes
phylum in sow-reared pigs housed in outdoor environments compared to littermates housed
in isolators with daily antibiotic treatment. Within the Firmicutes phylum, the most
compelling observation was the abundance of lactobacilli in animals reared in the
outdoor environment. Lactobacilli are often associated with the suckling pig and early
stages of colonization in the gastrointestinal tract. In this study, the high abundance
of lactobacilli in the fecal samples obtained from truly adult sows identified lactobacilli
as normal colonizers of the adult pig microbiota in the outdoor environment. Leser
et al. [28] found similar high-abundance phylotypes associated with the ileum, including L. amylovorous, L. johnsonii and L. reuteri, in pigs from different rearing environments. Our study further revealed that an
increase in hygiene status in pigs housed both indoor and in isolators with antibiotic
administration was associated with a significant decrease in mucosa-adherent lactobacilli.
Affected species included L. reuteri, L. delbrueckii, L. amylovorous, L. johnsonii and L. mucosae.

The reduced microbial diversity in outdoor animals compared to indoor and isolator
housed groups was a somewhat surprising outcome. These outdoor animals were exposed
to a huge variety of different bacterial species, as well as fungi, Archaea and viruses,
originating from both maternal and environmental sources. The soil especially is hugely
abundant in micro-organisms, and estimates of soil diversity show the presence of
at least 32 phyla, the dominant members of which are Proteobacteria, Bacteroidetes
and Firmicutes [31]. Soil ecosystems potentially provide an important source of microbes for gut colonization
of outdoor animals. However, only a selective subset of environmental bacteria colonize
the intestine, since we noted that the pig gut microbiota was comprised of a restricted
number of phyla, dominated by Bacteroidetes and Firmicutes, consistent with published
findings on the diversity of the adult human gut [29]. Current thinking has focussed on the benefits of a highly diverse gut microbiota,
as it has long been considered that this confers greater plasticity of the bacterial
community to respond to perturbations within the gut ecosystem [17]. Paradoxically, we found that exposure to a large variety of environmental microbes
in early life does not generate greater diversity in the adult gut but rather leads
to a microbiota that is dominated by a limited number of phyla composed of bacteria
with proven health-promoting properties.

Lactobacilli have long been known for their health-promoting effects and they directly
limit the prevalence of several intestinal pathogens including E. coli and salmonella [32-34]. In this study, L. reuteri was one of the most abundant members of the mucosa-adherent microbiota of the outdoor
group. Reuterin, a broad-spectrum antimicrobial substance, is produced by L. reuteri [35] and inhibits most intestinal bacteria with the exception of Lactobacillus strains [36]. Importantly, the greater abundance of L. reuteri in the outdoor animals may contribute to the enhanced presence of other Lactobacillus species as well as the decreased microbial diversity observed in these animals. A
further point meriting comment is the reduced presence of potentially pathogenic phylotypes
in outdoor-housed pigs. These phylotypes were clearly present in both indoor and isolator
housed animals, although animals showed no overt signs of infection. The specific
reduction in Firmicutes, in particular lactobacilli, in these pigs may affect the
normal mechanisms of colonization resistance that control potentially pathogenic populations
within the gut ecosystem.

Although there has been a major focus on health-promoting probiotic actions of lactobacilli
following their introduction as oral supplements, significantly less attention has
been paid to the effects of naturally-acquired, gut-colonizing (autochthonous) lactobacilli.
Given that immune modulation is dependent on gut colonization, close proximity to
the mucosa and host adaptation, naturally-acquired lactobacilli clearly deserve greater
attention. Of those species studied, L. casei, L. johnsonii and L. plantarum are strong inducers of IL-12 and/or INF-γ, thereby favouring a Th1 cytokine profile
[37,38]. Conversely, L. reuteri inhibits the induction of IL-12 and TNF-α and also attenuates L. casei-induced IL-12 [38]. A fine balance between Th1-polarising lactobacilli strains and those which counterbalance
such responses may be an important factor in maintaining mucosal immune homeostasis
and explain the lack of overt Th1 or Th2 responses in outdoor-housed pigs in the current
study.

While there was no evidence of Th1/Th2 pathways being affected, we found significant
effects of environment on the Type 1 interferon (IFN) signalling pathways. Isolator-reared
pigs exhibited increased gene expression levels of the IFNα/β transcription/signalling
factors IRF7 and USP18. Type 1 IFN signalling induces the expression of a large number of target genes,
which in the current study included MX2, G1P2, ISG20, FAM14A, IFIT2 and IFIT3. Three Type 1 IFN-inducible genes (IFRD1, OAS1 and IFIT2) were increased in indoor-housed animals compared to outdoor-housed animals, indicating
that the IFNα/β pathway is directly affected by the housing environment. A number
of recent studies further support our data describing the influence of the gut microbiota
on the Type 1 IFN pathway. For example, conventionalized pigs exhibited increased
expression of IRF7, STAT1 and STAT2 when compared with their germ-free counterparts [39]. Conversely, bacterial colonization of germ-free mice led to a decreased expression
of the IFN-related genes IRF7, ISGF3G, IFIT1 and STAT1[40]. Our study further qualifies these findings by establishing that specific microbial
composition, rather than the microbiota as such, influences Type 1 IFN signalling
during early colonization and development.

Type 1 IFNs have many biological properties, including innate, cellular and humoral
adaptive immune responses [41]. Much evidence has focussed on their central role in pathogen resistance, especially
viral immunity through recognition of dsRNA. The significance of Type 1 IFNs in response
to bacterial colonization and infection is receiving increasingly more attention [42,43]. IFN expression is induced in numerous cell lineages, including macrophages and plasmacytoid
dendritic cells, by bacterial components such as LPS and CpG DNA [44-46]. It is worth noting that the transcriptome analysis was performed on whole ileal
tissue samples, rather than on a specific cell subset. In this study we elected to
study interactions and contributions of all cell lineages present in the gut to comprehensively
characterize the transcriptomic changes induced by different microbiota compositions.
However, the contribution of individual lineages such as plasmacytoid dendritic cells
(DCs), which naturally produce Type 1 IFN, will be addressed in subsequent studies.

IFN-α/β has profound effects on immune cell development [41] by regulating the differentiation of B and T cells, myeloid DCs and natural killer
cells. Activation of immature DCs by IFN-α/β upregulates major histocompatibility
complex (MHC) class I. Consistent with this, we found that antigen presentation by
MHC class I was also affected by the microbiota and was upregulated in indoor reared
animals which also displayed increased Type 1 IFN levels. MHC class I molecules are
Type 1 IFN-inducible genes whose promoter regions contain typical IFN-stimulated response
elements (ISREs). MHC class I molecules are specialized for presentation of endogenously
synthesized proteins, including self-proteins, to the TCR of CD8+ T-cells [47]. The cross-presentation of antigens on MHC class I molecules, the induction of CTL
responses and the subsequent memory CD8+ T cell survival are also dependent on IFN-α/β.

Increased expression of MHC class I in the indoor environment was accompanied by the
upregulation of a plethora of chemokines, including CCL2, CCL8, CCL28, CCR1, CXCR4 and CXCL12. Chemokines are chemotactic cytokines that function during immune responses to recruit
effector cells to sites of inflammation and infection. They are involved in the pathophysiology
of many diseases. Numerous chemokines have been implicated in the pathology and perpetuation
of tissue destructive inflammatory processes in patients with IBD, including CCL2 [48] and CCL8 [49]. Increased expression of these chemokines in the indoor-housed animals indicates
the presence of an immune-activated gut microenvironment. This contrasts with the
lack of innate and pro-inflammatory gene expression in the outdoor-housed animals,
which may be indicative of a more immune-tolerant and homeostatic mucosal immune system
in these animals. Further studies are required to assess the impact of the microbiota,
immune gene transcription and immune cell lineages on specific tolerance towards food
and environmental antigens and long-term predisposition to infection, food intolerance
and allergy.

Conclusion

Environmental exposure in early life has a significant impact on microbiota composition
of the adult gut and the immune transcriptome during development. Rural, outdoor environments
support the establishment of a natural microbiota dominated by lactobacilli and containing
low numbers of potentially pathogenic bacteria and this may be an important factor
in maintaining mucosal immune homeostasis and limiting excessive inflammatory responses
in the gut. The significance of the microbiome and transcriptome data presented herein
in relation to immune events such as oral tolerance and host defence against enteric
pathogens is a major focus of our future studies.

Methods

Experimental animals and tissue collection

Twelve Large White × Landrace sows (Sus scrofa) were housed at either an indoor (intensive) or an outdoor (extensive) facility.
The sows were artificially inseminated by the same boar to minimize genetic variation
among the offspring. Three piglets from each outdoor-housed sow (OUT) and indoor-housed
sow (IN) were left to suckle with the mother until day 28, when all piglets were weaned.
Three piglets from each indoor-housed sow (18 piglets in total) were transferred to
individual isolator units at the School of Clinical Veterinary Science (University
of Bristol, UK) at 24 hours of age. These piglets were given a daily dose of antibiotic
cocktail (Baytril (Bayer Healthcare, Uxbridge, UK) and Amoxinsol 50 (Vétoquinol UK
Ltd., Buckingham, UK)) for the duration of the study. Up until day 28, the isolator-housed
piglets (IR) were fed commercial porcine milk replacer (PiggiMilk, Parnutt Foods Ltd.,
Sleaford, UK) dispensed by an automated liquid feeding system. From day 29 onwards,
all piglets were fed creep feed (Multiwean, SCA Nutrition Ltd, Thirsk, UK) ad libitum. The experiment was run in three consecutive replicates, using four sows and 18 piglets
in every replicate.

Six randomly chosen piglets per treatment group were sacrificed by injection of sodium
pentobarbitone (Euthesate, Willows Francis Veterinary Ltd, Crawley, UK) at time-points
on day 5, 28 and 56. The ileum, defined as the region corresponding to 75% in length
from the pyloric sphincter, was excised. Detailed molecular analysis was performed
on this site as it represents a key region involved in both immune-inductive and effector
activities, including bacterial antigen sampling. Two ileal tissue samples were taken
and either washed in ice-cold phosphate buffered saline (PBS)/0.1% Tween 20 (Sigma-Aldrich,
Gillingham, UK) for construction of mucosa-associated 16S rRNA gene libraries (10-cm
piece) or processed in ice-cold PBS and transferred to RNAlater (Applied Biosystems,
Warrington, UK) for Affymetrix microarray and Real-time PCR studies (2-cm piece).
All animal work was performed according to the institutional and Home Office UK ethical
guidelines.

Analysis of the mucosal microbiota

Gut contents (N = 4 per treatment group) were removed from the ileum, and the tissue
was washed with ice-cold PBS and incubated in ice-cold PBS/0.1% Tween 20 overnight.
Detached bacteria were harvested by centrifugation at 10,000 × g for 10 min at 4°C.
Total DNA from the pellet was isolated using a DNA Spin Kit for Soil® (QBiogene Inc., Cambridge, UK) according to the manufacturer's protocol. PCR amplification
of the 16S rRNA genes was carried out with the universal primer set S-D-Bact-0008-a-S-20
(5'-AGAGTTTGATCMTGGCTCAG-3'; positions 8 to 27 in the Escherichia coli 16S rRNA gene) and S-*-Univ-1492-a-A-19 (5'-ACGGCTACCTTGTTACGACTT-3'; positions 1510
to 1492) [50]. Primer positions are represented according to the OPD nomenclature [51]. PCR cycling conditions were one cycle at 94°C for 5 min, followed by 25 cycles at
94°C for 30 sec, 57°C for 30 sec, 72°C for 2 min, with a final extension at 72°C for
10 min. PCR products were purified with the Wizard® SV Gel & PCR Clean-up System (Promega, Southampton, UK), cloned into the pCR-4 cloning
vector and transformed into E. coli TOP 10 chemically competent cells (TOPO TA Cloning Kit; Invitrogen, Paisley, UK) according
to the manufacturer's instructions. Recombinant colonies were picked and the inserts
were sequenced in the RINH genomics facility (University of Aberdeen, UK) using the
primer set S-*-Univ-0907-a-A-20 (5'CCGTCAATTCATTTGAGTTT-3') and S-*-Univ-0519-a-A-18
(5'-GWATTACCGCGGCKGCTG-3') [50]. All clone libraries were constructed under identical conditions in order to minimize
sample-to-sample variation, thus the relative differences in microbial composition
between the samples truly reflect animal treatment differences.

Enumeration of Lactobacillus species

Approximately 70 mg gut contents from IR and OUT animals at day 56 from both the ileum
(IR: N = 3; OUT: N = 2) and the colon (IR: N = 4; OUT: N = 3) were transferred to
Hungate tubes containing 2 ml of MRS broth/0.2% Tween 80 (Oxoid, Basingstoke, UK)
and dispersed by vortexing. The gut content suspensions were diluted in a series of
seven sequential ten-fold dilutions. Twenty microlitre aliquots of the dilutions were
plated out on MRS agar plates and dried off. The plates were placed in an anaerobic
gas jar and incubated at 37°C. Plates were read and data recorded and calculated after
48 hours of incubation.

The resulting 16S rRNA gene contigs were aligned using Multiple Sequence Comparison
by Log-Expectation (MUSCLE, http://www.ebi.ac.uk/Tools/musclewebcite[53]) and the alignments were inspected manually. The distance matrix (generated from
the multiple sequence alignment) was calculated using the Dnadist application of the
Phylogeny Inference Package http://evolution.genetics.washington.edu/phylip.htmlwebcite and Jukes-Cantor distance of 0.01. This stringent phylotype definition at 99% cut-off
was used in part because evidence suggests that bacteria with nearly-identical 16S
rRNA sequences may represent variable genotypes and different species [29].

Rarefaction and collector's curves of observed phylotypes, richness estimates and
diversity indices were determined with the DOTUR program [54] using Jukes-Cantor corrected distance matrix. The bias-corrected Chao 1 richness
estimator was calculated after 1000 randomizations of sampling without replacement.
Collector's curves of observed and estimated (Chao 1 and the abundance-based coverage
estimator, ACE) richness were constructed. Diversity was estimated using the Shannon
(H) and Simpson indices (D). The Simpson reciprocal index was calculated as 1/D, and
another version of the Simpson diversity index as 1-D. The Good's coverage percentage
was calculated with the formula [1-(n/N)] × 100, where n is the number of phylotypes
in a sample represented by one clone (singletons) and N is the total number of sequences
in that sample [55].

Similarity search of the 16S rRNA gene sequences against database entries was performed
using the BLAST program at the National Center for Biotechnology Information (NCBI)
website http://www.ncbi.nlm.nih.gov/BLASTwebcite. By using a >99% sequence similarity criterion, the sequences were assigned to the
respective bacterial phylotypes.

Phylotype comparisons were made among groups of subjects using the Mann-Whitney U
test. Multiple comparisons were carried out using the Kruskal-Wallis test, with P < 0.05 considered statistically significant.

Microarray hybridizations and data analysis

Ileal tissue (200 mg) (N = 6 per treatment group/time-point) was removed from RNAlater
and lyzed in Trizol (Invitrogen). RNA was isolated using standard chloroform/isopropanol
steps. Total RNA was further extracted with the RNeasy kit (Qiagen, Crawley, UK) according
to the manufacturer's instructions, including an RNase-free DNase I (Qiagen) digestion
step. RNA integrity was determined using the Agilent 2100 Bioanalyzer (Agilent Technologies,
Wokingham, UK).

Eight microgram of total RNA was reverse transcribed to cDNA and then transcribed
into biotin-labelled cRNA using the One-Cycle Target Labeling Kit (Affymetrix, Santa
Clara, CA) according to the manufacturer's instructions. cRNA quality was determined
by Agilent 2100 Bioanalyzer. Hybridization to the GeneChip Porcine Genome Array (Affymetrix)
on a GeneChip Fluidics Station 450 (Affymetrix) was performed at the Institute of
Medical Sciences Microarray Core Facility (University of Aberdeen, UK). Chips were
scanned with an Affymetrix GeneChip Scanner 3000 (Affymetrix). Image quality analysis
was performed using Gene Chip Operating Software (GCOS) (Affymetrix).

Further quality analysis, normalization by GeneChip Robust Multiarray Averaging (gcRMA),
statistical analysis and heatmap generation was performed with the freely available
software packages R http://www.r-project.orgwebcite and Bioconductor http://www.bioconductor.orgwebcite[56]. In particular we used the moderated F-test provided by the Bioconductor package
limma to test for differential expression [57].

Statistical analysis was performed separately for each of the three time-points (day
5, 28 and 56) on the two group comparisons IR vs OUT and IN vs OUT. As detailed in
the first Methods subsection, the animal experiments consisted of three replicates
with two piglets in each of the three experimental groups. This has created a three-group
design, with six biologically independent samples in each group and replicate as an
additional blocking factor.

To address the multiple testing issue the Storey method [58] was used to calculate q-values, as implemented in the Bioconductor package qvalue. This method gives estimates of the associated false discovery rate for a given cut-off.
Although these q-values are shown in Additional file 2, the lists of differentially expressed genes were not based only on q-values or P-values, but tried to address the balance between statistical significance and biological
relevance. Thus, differences in gene expression between treatments were determined
using a cut-off of P < 0.01 and -2 ≤ fold change ≥ 2. This approach is very much in line with recommendations
based on the Micorarray Quality Control study (MAQC) [59], which recommends the use of fold change ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible differentially
expressed gene lists.

Functional analysis of microarray data

Gene Ontology (GO) based functional interpretation of the data was performed using
the Database for Annotation, Visualization and Integrated Discovery (DAVID 2006; http://david.abcc.ncifcrf.govwebcite), an expanded version of the original web-accessible programs described by Dennis
et al. [60]. Significantly different transcripts (P < 0.05) were allocated into the GO category Biological Process to unearth patterns of gene expression significantly enriched for specific GO terms.

All differentially expressed genes (P < 0.05) were imported into MetaCore analytical software (GeneGo, St Joseph, MI) to
generate pathway maps. MetaCore is a proprietary, manually curated database containing
human protein-protein, protein-DNA and protein compound interactions, metabolic and
signalling pathways, and the effects of bioactive molecules. MetaCore software contains
approximately 450 canonical signalling and metabolic pathways. Porcine Affymetrix
probeset IDs were converted into human Affymetrix probeset IDs using annotation supplied
by Tsai et al. [61]. Integrated pathway enrichment analysis was performed using the knowledge-based canonical
pathways and endogenous metabolic pathways. Ranking of relevant integrated pathways
was based on P-values calculated using hypergeometric distribution. P-values represented the probability of a given number of genes from the input list
to match a certain number of genes in the map by chance, considering the numbers of
genes in the experiment versus the number of genes in the map within the full set
of all genes on maps.

Real-time PCR analysis of differentially expressed genes

The mRNA levels differentially expressed between the treatment groups in microarray
analyses were further validated using Real-time PCR. Two micrograms of total RNA isolated
from the ileum (N = 6, isolated for microarray analysis) was reverse transcribed into
cDNA using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems) with
random primers. Real-time PCR analysis was performed using a 7500 Fast Real-Time PCR
System (Applied Biosystems) with the Power SYBR Green PCR Master Mix (Applied Biosystems)
according to the manufacturer's recommendations. Primers (Sigma-Aldrich; Additional
file 3) were designed for the porcine sequence of interest using Primer Express Software
v3.0 (Applied Biosystems). PCR cycling conditions were one cycle at 95°C for 10 min,
followed by 40 cycles at 95°C for 15 sec and 60°C for 1 min, ending with a dissociation
step. All samples were run in triplicate. EEF1A1 was selected as a reference gene for normalization due to its low variation between
samples in the microarray analysis.

Data were analyzed on a logarithmic scale with base 2 by Student's t-test allowing
for unequal variances with P < 0.05 considered statistically significant. Standard errors of differences were also
calculated on this scale. Differences were back-transformed to calculate fold changes.

Authors' contributions

DK, CRS, MB, JRP and BPG conceived and coordinated the study. IEM carried out the
microarray experiment, bioinformatics analysis and RT-PCR work and drafted the manuscript
along with BS and DK. BS carried out the 16S rRNA gene library preparation, constructed
sequence contigs and performed phylogenetic identification. CRS, ML and MB conducted
the animal trial. CDM helped with the statistical design of the study and developed
the analysis strategy for the microarray and Real-time PCR data. CCM carried out the
phylogenetic analysis of clone libraries. RIA and JIP were involved in technical and
scientific discussion of the project. All authors read and approved the final manuscript.

Acknowledgements

We thank Dr Elaina Collie-Duguid and Diane Stewart at the Institute of Medical Sciences
Microarray Core facility (University of Aberdeen) for Affymetrix microarray hybridization
and chip processing. We also thank Dr George Grant at the RINH Gut Immunology Group
(University of Aberdeen) for his work on the enumeration of Lactobacillus species and Pauline Young at the RINH Genomics Facility (University of Aberdeen) for
sequencing of bacterial clones.

This work was supported by a joint grant from the Department for Environment, Food
and Rural Affairs (DEFRA) and the Meat and Livestock Commission (MLC) to IEM and BS
(LS3658/CSA 6738), and the Scottish Government Rural and Environment Research and
Analysis Directorate (RERAD) to DK.

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